mcp-multiagent-bridge

dannystocker/mcp-multiagent-bridge

3.2

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MCP Multiagent Bridge is a lightweight Python server designed for secure coordination of multiple LLM agents using the Model Context Protocol (MCP).

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MCP Multiagent Bridge

Production-ready Python MCP server for secure multi-agent coordination with comprehensive safeguards.

Overview

Enables multiple LLM agents (Claude, Codex, GPT, etc.) to collaborate safely through the Model Context Protocol without sharing workspaces or credentials. Built with security-first architecture and production-grade safeguards.

Use cases:

  • Backend agent coordinating with frontend agent on different codebases
  • Security review agent validating changes from development agent
  • Specialized agents collaborating on complex multi-step workflows
  • Any scenario requiring isolated agents to communicate securely

Key Features

🔒 Security Architecture

Authentication & Authorization:

  • HMAC-SHA256 session token authentication
  • Automatic secret redaction (API keys, passwords, tokens, private keys)
  • 3-hour session expiration with automatic cleanup
  • SQLite WAL mode for atomic, race-condition-free operations

4-Stage YOLO Guard™: Command execution (optional) requires multiple confirmation layers:

  1. Environment gate - explicit YOLO_MODE=1 opt-in
  2. Interactive typed confirmation phrase
  3. One-time validation code (prevents automation)
  4. Time-limited approval tokens (5-minute TTL, single-use)

Rate Limiting:

  • Token bucket algorithm with configurable windows
  • Default: 10 requests/minute, 100/hour, 500/day
  • Per-session tracking with automatic reset
  • Prevents abuse while allowing legitimate bursts

Audit Trail:

  • Comprehensive JSONL logging of all operations
  • Timestamps, session IDs, actions, results
  • Tamper-evident sequential logging
  • Supports compliance and forensic analysis

🏗️ Production-Ready Architecture

  • Message-only bridge - No auto-execution, returns proposals only
  • Schema validation - Strict JSON schemas for all MCP tools
  • Command validation - Configurable whitelist/blacklist patterns
  • Comprehensive error handling - Graceful degradation, informative errors
  • Extensible design - Plugin architecture for future backends

📦 Platform Support

Works with any MCP-compatible LLM:

  • Claude Code, Claude Desktop, Claude API
  • OpenAI models (via MCP adapters)
  • Anthropic API models
  • Custom/future models (not tied to specific backend)

Installation

# Clone repository
git clone https://github.com/dannystocker/mcp-multiagent-bridge.git
cd mcp-multiagent-bridge

# Install dependencies
pip install mcp>=1.0.0

# Run tests
python test_security.py

Full setup: See

Documentation

Getting Started:

  • - 5-minute setup guide
  • - Real-world collaboration scenarios
  • - Production deployment & test results ⭐ NEW

Production Hardening:

  • - Keep-alive daemons, watchdog, task reassignment ⭐ NEW
  • - Complete test results with IF.TTT citations

Security & Compliance:

  • - Threat model, responsible disclosure policy
  • - Command execution safety guide
  • Policy compliance: Anthropic AUP, OpenAI Usage Policies

Contributing:

  • - Development setup, PR workflow
  • - MIT License

Technical Stack

  • Python 3.11+ - Modern Python with type hints
  • SQLite - Atomic operations with WAL mode
  • MCP Protocol - Model Context Protocol integration
  • pytest - Comprehensive test suite
  • CI/CD - GitHub Actions (tests, security scanning, linting)

Project Statistics

  • Lines of Code: ~6,700 (including tests, production scripts + documentation)
  • Test Coverage: ✅ Core security validated (482 operations, zero failures)
  • Documentation: 3,500+ lines across 11 markdown files
  • Dependencies: 1 (mcp>=1.0.0, pinned for reproducibility)
  • License: MIT

Production Test Results (November 2025)

10-Agent Stress Test:

  • ✅ 1.7ms average latency (58x better than 100ms target)
  • ✅ 100% message delivery (zero failures)
  • ✅ 482 concurrent operations (zero race conditions)
  • ✅ Perfect data integrity (SQLite WAL validated)

9-Agent S² Production Hardening:

  • ✅ 90-minute test (idle recovery, keep-alive, watchdog)
  • ✅ <5 min task reassignment (automated worker failure recovery)
  • ✅ 100% keep-alive delivery (30-minute validation)
  • ✅ <50ms push notifications (filesystem watcher, 428x faster than polling)

Full Report: See

Development

# Install dev dependencies
pip install -r requirements.txt

# Install pre-commit hooks
pip install pre-commit
pre-commit install

# Run test suite
pytest

# Run security tests
python test_security.py

See for complete development workflow.

Production Status

✅ Production-Ready (Validated November 2025)

Successfully tested with:

  • ✅ 10-agent stress test (94 seconds, 100% reliability)
  • ✅ 9-agent production deployment (90 minutes, full hardening)
  • ✅ 1.7ms average latency (58x better than target)
  • ✅ Zero data corruption in 482 concurrent operations
  • ✅ Automated recovery from worker failures (<5 min)

Recommended for:

  • Production multi-agent coordination
  • Development and testing workflows
  • Isolated workspaces (recommended)
  • Human-supervised operations
  • 24/7 autonomous agent systems (with production scripts)

Production deployment:

  • See for complete deployment guide
  • Use for keep-alive, watchdog, and task reassignment
  • Follow security best practices

Support

License

MIT License - Copyright © 2025 Danny Stocker

See for full terms.

Acknowledgments

Built with Claude Code and Model Context Protocol.